As an environmentally friendly top-notch workout, Larsen (2013) contends that bike-sharing companies offer a cost-effective way of promoting a sustainable lifestyle in urban areas. It goes without saying that encouraging the masses to utilize bicycles in their daily errands not only promotes their health but also plays a huge part in reducing carbon emissions.
In this project, I utilized R programming language to analyze bike riding data from a fictional bike-sharing company in Chicago, Illinois. Cyclistic is a bike-share program offering three product categories that include reclining bikes, hand tricycles, and cargo bikes. The company has 5824 available bikes and 692 docking stations. The main business task is to generate revenue through increased annual subscriptions by converting casual riders to annual members. Cyclistic historical trip data is hosted on their database and contains information about rideable bike types, the start station, and the end station name with coordinates, their start and end riding time, their membership status, and the trip id. The dataset provided under this license is open-source and can be used by industry practitioners The stakeholders include: Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them.Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.
Cyclistic’s historical trip data of the [last 12 months] (https://divvy-tripdata.s3.amazonaws.com/index.html). The data is available in .csv format and containts dates and times, with no outliers and relevant to the project. The data has been made available by Motivate International Inc. under [this license] (https://www.divvybikes.com/data-license-agreement). This is public data that you can use to explore how different customer types are using Cyclistic bikes.
1. A clear statement of the business task
2. A description of all data sources used
3. Documentation of any cleaning or manipulation of data
4. A summary of your analysis
5. Supporting visualizations and key findings
6. Your top three recommendations based on your analysis
The codebase contains three main files: • An R file containing data analysis and visualizations (with related comments),accessed [here] • An [R Markdown] file containing the case study project activities step by step with code chunks.
• Casual riders had longer trip duration's than annual members.
• Saturday was the best riding day for casual riders and Sunday for annual members.
• Bike use usually peaks at rush hour times, between 3pm to 7pm.
• Months around summer have the most rides by both user categories and winter is the least busiest.
• The most utilized bike are docked bikes with most trips and longer riding duration's.
• Casual riders utilize the bikes more than annual subscribers in all bike categories.
• Clark St & Elm St represents the most used station by members.
• Streeter Dr & Grand Ave is the most used station by casuals.
• The marketing team should target casual riders over the weekend during the summer and autumn seasons.
• Approach casual riders at Streeter Dr and Grand Avenue as a start station and Clark St & Elm St as the end station
• Aim to stock more docked bikes and perhaps promote classic bikes to the new cohort since casual riders ride for longer durations.
• Include a public health promotion campaign for bike riding benefits.
• Aim to launch the new marketing campaign at peak hours between 3-7pm .
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent